Sensing and perception technology to enable real time monitoring of passenger movement behaviours through congested rail stations

The real time monitoring of passenger movement and behaviour through public transport environments including precincts, concourses, platforms and train vestibules would enable operators to more effectively manage congestion at a whole-of-station level. While existing crowd monitoring technologies allow operators to monitor crowd densities at critical locations and react to overcrowding incidents, they do not necessarily provide an understanding of the cause of such issues. Congestion is a complex phenomenon involving the movements of many people though a set of spaces and monitoring these spaces requires tracking large numbers of individuals. To do this, traditional surveillance technologies might be used but at the expense of introducing privacy concerns. Scalability is also a problem, as complete sensor coverage of entire rail station precinct, concourse and platform areas potentially requires a high number of sensors, increasing costs. In light of this, there is a need for sensing technology that collects data from a set of 'sparse sensors', each with a limited field of view, but which is capable of forming a network that can track the movement and behaviour of high numbers of associated individuals in a privacy sensitive manner. This paper presents work towards the core crowd sensing and perception technology needed to enable such a capability. Building on previous research using three-dimensional (3D) depth camera data for person detection, a privacy friendly approach to tracking and recognising individuals is discussed. The use of a head-to-shoulder signature is proposed to enable association between sensors. Our efforts to improve the reliability of this measure for this task are outlined and validated using data captured at Brisbane Central rail station.

[1]  Alexander Virgona,et al.  Head-to-shoulder signature for person recognition , 2012, 2012 IEEE International Conference on Robotics and Automation.

[2]  Nathan Kirchner,et al.  Robust and efficient people detection with 3-D range data using shape matching , 2010 .

[3]  James Partridge,et al.  Estimating the costs of over-crowding on Melbourne's rail system , 2013 .

[4]  A. Alempijevic,et al.  A robust people detection, tracking, and counting system , 2014 .

[5]  Phil Charles,et al.  Spreading peak demand for urban rail transit through differential fare policy : a review of empirical evidence , 2013 .

[6]  Emmanuel Dellandréa,et al.  A People Counting System Based on Face Detection and Tracking in a Video , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[7]  Andrea Cavallaro,et al.  Person re-identification in crowd , 2012, Pattern Recognit. Lett..

[8]  James Gray,et al.  Rail simulation and the analysis of capacity metrics , 2013 .

[9]  Robert Fitch,et al.  Bootstrapping navigation and path planning using human positional traces , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Takayuki Kanda,et al.  Person Tracking in Large Public Spaces Using 3-D Range Sensors , 2013, IEEE Transactions on Human-Machine Systems.

[11]  Baoji Wang,et al.  Developing a train crowding economic costing model and estimating passenger crowding cost of Sydney CityRail network , 2012 .

[12]  B. Boghossian,et al.  The challenges of robust 24/7 video surveillance systems , 2005 .